Abstract
This work proposes an innovative methodology for the optimal placement of grid-forming converters (GFM) in converter-dominated grids while accounting for multiple stability classes. A heuristic-based methodology is proposed to solve an optimisation problem whose objective function encompasses up to 4 stability indices obtained through the simulation of a shortlist of disturbances. The proposed methodology was employed in a modified version of the 39-bus test system, using DigSILENT Power Factory as the simulation engine. First, the GFM placement problem is solved individually for the different stability classes to highlight the underlying physical phenomena that explain the optimality of the solutions and evidence the need for a multi-class approach. Second, a multi-class approach that combines the different stability indices through linear scalarisation (weights), using the normalised distance of each index to its limit as a way to define its importance, is adopted. For all the proposed fitness function formulations, the method successfully converged to a balanced solution among the various stability classes, thereby enhancing overall system stability.

Abstract
The purpose of an Advanced Distribution Management System (ADMS) is to consolidate the key operational functions of a SCADA system, Outage management System (OMS) and Distribution Management System (DMS) into a unified platform. This includes several key functions: SCADA operation, incidents and outages management, teams and field works management including switching operations and advanced applications for network analysis and optimization. The new generation of ADMS also implements a predictive operation strategy to enhance real-time operator responsiveness. The innovative aspects related to the new generation of ADMS built on top of an open architecture will be presented in this paper. © The Institution of Engineering & Technology 2025.

Abstract
A Renewable Energy Management System (REMS) is designed to enhance the operation and efficiency of renewable energy assets, such as wind and solar power, by addressing their inherent variability. Through integration with Supervisory Control and Data Acquisition (SCADA) systems, REMS facilitates real-time adjustments and forecast-based decisions, enabling grid security, optimizing energy dispatch, and maximizing economic benefits. This paper introduces a versatile active power control methodology for renewable energy plants, capable of operating across various time scales to address technical and market-driven requirements. The proposed framework processes inputs from power system measurements to generate forecasts using two distinct approaches, optimizing setpoints for energy dispatch and control processes. Four optimization methods—merit order, weighted allocation, proportional allocation, and linear optimization—are employed to maximize power utilization while adhering to system constraints. The approach is validated for two control intervals: 4 seconds, representing rapid response for converter-based resources, and 15 minutes, simulating broader operational adjustments for reserve provision programs. This dynamic and scalable control framework demonstrates its potential to enhance the management, efficiency, and sustainability of renewable energy systems. © The Institution of Engineering & Technology 2025.

Abstract
Flexibility markets are emerging across Europe to improve the efficiency and reliability of distribution networks. This paper presents a methodology that integrates local flexibility markets into network maintenance scheduling, optimizing the process by contracting flexibility to avoid technical issues under the topology defined to operate the network during maintenance. A meta-heuristic approach, Evolutionary Particle Swarm Optimization (EPSO), is used to determine the optimal network topology.

Abstract
This work proposes a robust methodology for the location and sizing of grid forming (GFM) converters that simultaneously considers the solution costs and the security gains while accounting for the TSO nonlinear cost-security sensitivity. Such methodology, which includes a collection of techniques to reduce the problem dimensionality, formulates the placement problem as a non-linear multi-criteria decision support problem and uses a solution-seeking algorithm based on Bayesian Optimisation to determine the solution. To ease comprehension, a modified version of the IEEE 39 Test System is used as a case study throughout the method's detailed explanation and application example. A sensitivity analysis of the GFM converter's over-current capacity in the solution of the formulated placement problem is also performed. The results show that the proposed method is successful in finding solutions with physical meaning and that respect the decision agent preferences.

Abstract
Electric vehicles (EVs) are crucial in achieving a low-carbon transportation sector and can inherently offer demand-side flexibility by responding to price signals and incentives, yet real-world strategies to influence charging behavior remain limited. This paper combines bilevel optimization and causal machine learning as complementary tools to design and evaluate dynamic incentive schemes as part of a pilot project using a supermarket's EV charging station network. The bilevel model determines discount levels, while double machine learning quantifies the causal impact of these incentives on charging demand. The results indicate a marginal increase of 1.16 kW in charging demand for each one-percentage-point increase in discount. User response varies by hour and weekday, revealing treatment effect heterogeneity, insights that can inform business decision-making. While the two methods are applied independently, their combined use provides a framework for connecting optimization-based incentive design with data-driven causal evaluation. By isolating the impact of incentives from other drivers, the study sheds light on the potential of incentives to enhance demand-side flexibility in the electric mobility ecosystem.